1. Technical Field
This invention relates to the field of signal processing. More particularly, this invention relates to processing measured signals to remove unwanted signal components caused by noise and especially noise caused by motion artifacts.
2. State of the Art
The measurement of physiological signals can often be difficult because the underlying physiological processes may generate very low level signals. Furthermore, interfering noise is inherent in the body and the interface between the body and sensors of physiological processes, Examples of physiological measurements include: measurement of electrocardiogram (ECG) signals based on the electrical depolarization of the heart muscle, blood pressure, blood oxygen saturation, partial pressure of CO2, heart rate, respiration rate, and depth of anesthesia. ECG signals, for example, are typically detected by surface electrodes mounted on the chest of a patient. ECG signals are weak at the signal source (i.e., the heart) and are even weaker at the surface of the chest. Furthermore, electrical interference from the activity of other muscles (e.g., noise caused by patient breathing, general movement, etc.) causes additional interference with physiological signals such as an ECG. Thus, considerable care must be taken in the design and use of physiological processors to enhance the quality of the true signal and reduce the effects of interfering noise signals.
It is convenient to characterize a measured signal as being a composite signal composed of a true signal component and a noise signal component. The terms xe2x80x9cmeasured signalxe2x80x9d and xe2x80x9ccomposite signalxe2x80x9d will be used interchangeably hereinafter. Signal processors are frequently used to remove noise signal components from a composite measured signal in order to obtain a signal which closely, if not identically, represents the true signal. Conventional filtering techniques such as low pass, band pass, and high pass filtering can be used to remove noise signal components from the measured composite signal where the noise signal component occupies a frequency range outside the true signal component. More sophisticated techniques for conventional noise filtering include multiple notch filters, which are suitable for use where the noise signal component exists at multiple, distinct frequencies, all outside the true signal frequency band.
However, it is often the case that the frequency spectrum of the true and noise signal components overlap and that the statistical properties of both signal components change with time. More importantly, there are many cases where little is known about the noise signal component. In such cases, conventional filtering techniques are ineffective in extracting the true signal.
The measurement of oxygen saturation in the blood of a patient is a common physiological measurement the accuracy of which may be compromised by the presence of noise. Knowledge of blood oxygen saturation can be critical during surgery. There are means of obtaining blood oxygen saturation by invasive techniques, such as extracting and testing blood removed from a patient using a co-oximeter. But, such invasive means are typically time consuming, expensive, and uncomfortable for the patient. Fortunately, non-invasive measurements of blood oxygen saturation can be made using known properties of energy attenuation as a selected form of energy passes through a bodily medium. Such non-invasive measurements are performed routinely with a pulse oximeter.
The basic idea behind such energy attenuation measurements is as follows. Radiant energy is directed toward a bodily medium, where the medium is derived from or contained within a patient, and the amplitude of the energy transmitted through or reflected from the medium is then measured. The amount of attenuation of the incident energy caused by the medium is strongly dependent on the thickness and composition of the medium through which the energy must pass, as well as the specific form of energy selected. Information about a physiological system can be derived from data taken from the attenuated signal of the incident energy transmitted or reflected. However, the accuracy of such information is reduced where the measured signal includes noise. Furthermore, non-invasive measurements often do not afford the opportunity to selectively observe the interference causing the noise signal component, making it difficult to remove.
A pulse oximeter is one example of a physiological monitoring system which is based upon the measurement of energy attenuated by biological tissues and substances. More specifically, a pulse oximeter measures the variable absorption caused by arterial blood volume changes. Pulse oximeters transmit electromagnetic energy at two different wavelengths, typically at 660 nm (red) and 940 nm (infrared, hereinafter IR) into the tissue and measure the attenuation of the energy as a function of time. The output signal of a pulse oximeter is sensitive to the pulsatile portion of the arterial blood flow and contains a component which is a waveform representative of the patient""s arterial pulse. This type of signal, which contains a component related to the patient""s pulse, is called a plethysmographic waveform or plethysmogram.
Pulse oximetry measurements typically use a digit, such as a finger, or an ear lobe or other element of the body, where blood flows close to the skin as the medium through which light energy is transmitted. The finger, for example, is composed of various tissues and substances including skin, fat, bone, muscle, blood, etc. The extent to which each of these biological tissues and substances attenuate incident electromagnetic energy is generally known. However, the effect of motion can cause changes in the optical coupling of the sensor (or probe) to the finger, the underlying physiology, the local vasculature, optical properties of tissues due to changing optical path length as well as combinations and interactions of the all of the above. Thus, patient motion may cause erratic energy attenuation.
A typical pulse oximeter includes a sensor, cabling from the sensor to a computer for signal processing and visual display, the computer and visual display typically being included in a patient monitor. The sensor typically includes two light emitting diodes (LEDs) placed across a finger tip and a photodetector on the side opposite the LEDs. Each LED emits a light signal at different frequencies. The detector measures both transmitted light signals once they have passed through the finger. The signals are routed to a computer for analysis and. display of the various parameters measured.
The underlying physical basis of a pulse oximeter is Beer""s law (also referred to as Beer-Lambert""s or Bouguer""s law) which described attenuation of monochromatic light traveling through a uniform medium which absorbs light with the equation:
Itransmitted=Iincidentexe2x88x92dcxcex1(xcex),xe2x80x83xe2x80x83(1)
where Itransmitted is the intensity of the light transmitted through the uniform medium, Iincident is the intensity of incident light, d is the distance light is transmitted through the uniform medium, c is the concentration of the absorbing substance in the uniform medium, expressed in units of mmol Lxe2x88x921, and xcex1(xcex) is the extinction or absorption coefficient of the absorbing substance at wavelength xcex, expressed in units of L/(mmol cm). The properties of Beer""s law are valid even if more than one substance absorbs light in the medium. Each light absorbing substance contributes its part to the total absorbance.
Each LED emits light at different wavelengths, typically red (centered at about 660 nm) and IR (centered at about 940 nm) frequency bands. The intensity of light transmitted through tissue, Itransmitted, is different for each wavelength of light emitted by the LEDs. Oxyhemoglobin (oxygenated blood) tends to absorb IR light, whereas deoxyhemoglobin (deoxygenated blood) tends to absorb red light. Thus, the absorption of IR light relative to the red light increases with oxyhemoglobin. The ratio of the absorption coefficients can be used to determine the oxygen saturation of the blood.
To estimate blood oxygen saturation, SpO2, a two-solute concentration is assumed. A measure of functional blood oxygen saturation level, SpO2, can be defined as:                                           Sp            ⁢                          xe2x80x83                        ⁢                          O              2                                =                                    c              o                                                      c                r                            +                              c                o                                                    ,                            (        2        )            
where co represents oxyhemoglobin solute concentration, and Cr represents reduced or deoxyhemoglobin solute concentration.
Noise signal components in a measured pulse oximetry light signal can originate from both AC and DC sources. DC noise signal components may be caused by transmission of electromagnetic energy through tissues of relatively constant thickness within the body, e.g., bone, muscle, skin, blood, etc. Such DC noise signal components may be easily removed with conventional filtering techniques. AC noise signal components may occur when tissues being measured are perturbed and, thus, change in thickness while a measurement is being made. Such AC noise signal components are difficult to remove with conventional filtering techniques. Since most materials in and derived from the body are easily compressed, the thickness of such matter changes if the patient moves during a non-invasive physiological measurement. Thus, patient movement can cause the properties of energy attenuation to vary erratically. The erratic or unpredictable nature of motion artifacts induced by noise signal components is a major obstacle in removing them.
Various approaches to removing motion artifacts from measured physiological signals, and particularly for use in pulse oximeters, have been proposed. U.S. Pat. Nos. 5,482,036, 5,490,505, 5,632,272, 5,685,299, and 5,769,785, all to Diab et al., and U.S. Pat. No. 5,919,134 to Diab, disclose methods and apparatuses for removing motion artifacts using adaptive noise cancellation techniques. The basic proposition behind the Diab et al. approach is to first generate a noise reference signal from the two measured signals, and then use the noise reference signal as an input to an adaptive noise canceler along with either or both of the measured signals to remove the reference noise signal from the measured signals, thus approximating the actual parametric signals of interest. The Diab et al. approach appears to require the use of both measured input signals to generate a noise reference signal.
Another approach to noise artifact elimination is disclosed in U.S. Pat. No. 5,588,427 to Tien. Tien uses fractal dimension analysis to determine the complexity of waveforms in order to determine the proper value of the ratio of true intensities based on signal complexity. The Tien approach employs a fractal analyzer to determine values for two ratios, xcex1 and xcex2, based on the measured time varying intensity of the transmitted red and IR light signals including noise. xcex1 is defined as the ratio of the time varying true intensity of light transmitted from the red LED and the time varying true intensity of the light transmitted from the IR LED. xcex7 is a similar ratio relating the noise introduced during the measurement of the light transmitted by the red LED and the noise introduced during the measurement of the light transmitted by the IR LED. According to Tien, a fractal analyzer then determines values for xcex1 and xcex2 and provides (xcex1,xcex2) pairs to a statistical analyzer. The statistical analyzer performs analysis of one or more (xcex1,xcex2) pairs to determine the best value for xcex1, which is then provided to a look-up table. The look-up table provides a value corresponding to the arterial oxygen saturation in the patient. While the Tien approach appears to be an innovative use of fractal analysis, it also appears to be computationally complex.
Yet another approach to noise artifact elimination is disclosed in U.S. Pat. Nos. 5,885,213, 5,713,355, 5,555,882 and 5,368,224, all to Richardson et al. The basic proposition behind the Richardson et al. approach is to switch operative frequencies periodically based on evaluating the noise level associated with various possible frequencies of operation in order to select the frequency of operation that has the lowest associated noise level. It would appear that data measured at a noisy frequency, using the Richardson et al. approach may be invalid or useless for calculating arterial oxygen saturation. Furthermore, Richardson et al. requires a computational overhead to constantly monitor which frequency of operation provides the least noise.
Another approach to noise artifact elimination is disclosed in U.S. Pat. No. 5,853,364 to Baker, Jr et al. The Baker, Jr. et al. approach first calculates the heart rate of the patient using an adaptive comb filter, power spectrum and pattern matching. Once the heart rate is determined, the oximetty data is adaptively comb filtered so that only energy at integer multiples of the heart rate are processed. The comb filtered data and the raw oximetry data are filtered using a K nan filter to adaptively modify averaging weights and averaging times to attenuate motion artifact noise. The adaptive filtering of the Baker, Jr. et al. approach appears to add significant computational complexity to solve the problem of motion artifact rejection.
Still another approach to noise artifact elimination is disclosed in U.S. Pat. No. 5,431,170 to Mathews. Mathews couples a conventional pulse oximeter light transmitter and receiver with a transducer responsive to movement or vibration of the body. The transducer provides an electrical signal varying according to the body movements or vibrations, which is relatively independent of the blood or other fluid flow pulsations. Mathews then provides means for comparing the light signals measured with the transducer output and performing adaptive noise cancellation. An apparent disadvantage of the Mathews approach is the need for a secondary sensor to detect motion.
Thus, a need in the art exists for a method, apparatus and system to eliminate motion-induced noise artifacts from light signals, that is relatively simple computationally, and that does not require more than one sensor.
The present invention includes methods, apparatuses and systems for removing noise in physiological measurements caused by motion or other similar artifacts. The methods, apparatuses and systems of the present invention eliminate noise from light signals using a single conventional sensor and are relatively simple computationally.
In a method embodiment, a segment of data from a measured pulse oximetry signal is conventionally filtered, and frequency analyzed for major frequency components. The frequency components with the largest power spectral density are selected for subdividing into subsegments, each comprising an individual heartbeat. The subsegments are averaged and then analyzed to determine if the averaged subsegment is a valid pulse oximetry signal. Additionally, various quality or confidence measures may be used to evaluate the validity of such signal. Valid averaged subsegments become outputs for further processing to calculate physiological parameters such as blood oxygen saturation levels.
A circuit card embodiment includes a processor and memory for storing a computer program capable of executing instructions embodying the above method.
A system embodiment includes an input device, an output device, a memory device and a motion artifact rejection circuit card capable of executing instructions stored in the memory device implementing the methods described herein.
Finally, a system embodiment includes an input device, and output device, a memory device and a processor, which may be a digital signal processor, capable of executing instructions stored in the memory device implementing the methods described herein.